Player Identification in Hockey Broadcast Videos

TL;DR

This paper introduces a deep learning approach combining ResNet and LSTM to identify hockey players in broadcast videos by analyzing jersey numbers over time, achieving over 87% accuracy.

Contribution

It presents a novel end-to-end ResNet+LSTM model and a new hockey player dataset for improved player identification in challenging broadcast conditions.

Findings

Achieved over 87% accuracy on the new dataset.

Developed a ResNet+LSTM model for spatio-temporal jersey number recognition.

Created a new hockey player tracklet dataset for research.

Abstract

We present a deep recurrent convolutional neural network (CNN) approach to solve the problem of hockey player identification in NHL broadcast videos. Player identification is a difficult computer vision problem mainly because of the players' similar appearance, occlusion, and blurry facial and physical features. However, we can observe players' jersey numbers over time by processing variable length image sequences of players (aka 'tracklets'). We propose an end-to-end trainable ResNet+LSTM network, with a residual network (ResNet) base and a long short-term memory (LSTM) layer, to discover spatio-temporal features of jersey numbers over time and learn long-term dependencies. For this work, we created a new hockey player tracklet dataset that contains sequences of hockey player bounding boxes. Additionally, we employ a secondary 1-dimensional convolutional neural network classifier as a late score-level fusion method to classify the output of the ResNet+LSTM network. This achieves an overall player identification accuracy score over 87% on the test split of our new dataset.